Automatically detect anomalies like drifts and outliers within the data lake
Find and exclude anomalous data from AI-workloads, take action before it affects business
Spot anomalies before they impact your business
Monitor your data anomalies automatically without any setup using machine learning. No sampling, ML-based data anomaly monitoring without any setup.
- Column value level anomaly detection for things like value outliers, out-of-range values, and unexpected value
- Self-evolving thresholds based on ML models that understand your data, including seasonality
- Improved time-to-value as the model gets trained on historical data
Faster insights using predefined metrics
Telmai employs machine learning and statistical analysis to automatically generate thresholds for both custom and predefined data health metrics
- Metrics include schema drifts, data completeness, pattern drifts, and dozens more
- Telmai’s DQ metrics can be fully customized based on your team’s expectations
Detect drifts and anomalies in Business Metrics
Telmai supports monitoring of custom business metrics. Business metrics are aggregations applied over an existing schema attribute. This is applied to numeric data and grouped by one or many dimensions
Monitor the metrics that matter to your business
Telmai enables you to define business metrics that you want to track within a dataset
- You can apply various aggregation functions (sum, avg, min, max) to numeric data, tailoring the analysis to your specific needs
- Group your data by one or multiple dimensions to gain granular insights. Each dimension operates independently, ensuring comprehensive data analysis
Spot drifts the moment they occur
Telmai’s automated and manual thresholds ensure that data remains consistent, accurate, and compliant
- Using machine learning and statistical analysis, Telmai automatically builds thresholds for all custom and pre-defined metrics. These are advanced thresholds calculated using ML and statistical analysis of existing and historical data
- Automated thresholds allow you to take action on inconsistencies like excluding suspicious data from AI inputs
More features
Connect Datasources
Connect your datasource, or send data via REST, or load a local file
Analyze Data Health
Quickly identify and pinpoint data anomalies, errors, or inconsistencies
Alert
Telmai will learn your data and its trends and automatically alert on unexpected drifts
Recommendations
Telmai will finally advice you on next best actions for your data sets
Open Architecture
No-code connection to Data lake and Lakehouse –natively support raw formats like Iceberg, Hudi and Delta
Data Quality
Validate every value before ingesting into AI-model, automate and orchestrate DQ workflows in AI-workloads
Anomaly Detection
No sampling, ML-driven anomaly detection on column-values and business metrics
Data Layer Consistency
Improve quality across bronze, silver and gold layers embedded design patterns to stop bad data at bronze
Data Health Analysis
No code analysis and reporting of your data lake and lake house
Incident Management
Alerting, ticketing, investigation and remediation workflows